【摘要】 应用近红外光谱(NIRS)分析技术和偏最小二乘法(PLS)建立稻米脂肪定量分析数学模型,并比较糙米粒和糙米粉NIRS数学模型对预测稻米脂肪含量的效果差异。结果表明,当利用糙米粒和糙米粉NIRS数学模型对样品进行预测时,内部交叉验证预测值和真值之间的决定系数(R2)分别为94.44%和95.54%,内部交叉检验的标准差(RMSECV)分别为0.09%和0.08%;外部验证预测值和真值之间的R2值分别为79.51%和87.10%,预测标准差(RMSEP)分别为0.24%和0.26%,平均相对误差(ARE)分别为4.11%和3.30%。内部交叉验证和外部验证结果证明,糙米粒和糙米粉NIRS数学模型均具有较高的预测准确性,可应用于稻米营养品质改良实践。更多还原

【Abstract】 Based on NIRS technique and partial least squares (PLS) algorithm, two calibration models were established tanalyze quantitatively fat content (FT) in brown rice grain and brown rice flour, respectively. The determination coefficients (R2) othese two models for FT were 94.44% and 95.54%, and the root mean square errors of cross validation (RMSECV) were 0.09% an0.08%, respectively. In external validation, the R2 value between the true value and predicted value were 79.51% and 87.10% for Fin brown rice grain and brown rice flour, and the root mean square errors of prediction (RMSEP) were 0.24% and 0.26%, and thaverage relative errors were 4.11% and 3.30%, respectively. These results indicated that the method of NIRS has relatively higaccuracy in prediction tests for FT in brown rice grain and brown rice flour and that the two mathematic models established in thpresent study should be useful for nutrient quality improvement in rice breeding program.更多还原